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Comparison of Advanced Modeling Approaches for Autonomous Docking of Fully Actuated Vessels

  • This paper presents a systematic comparison of different advanced approaches for motion prediction of vessels for docking scenarios. Therefore, a conventional nonlinear gray-box-model, its extension to a hybrid model using an additional regression neural network (RNN) and a black-box-model only based on a RNN are compared. The optimal hyperparameters are found by grid search. The training and validation data for the different models is collected in full-scale experiments using the solar research vessel Solgenia. The performances of the different prediction models are compared in full-scale scenarios. %To use the investigated approaches for controller design, a general optimal control problem containing the advanced models is described. These can improve advanced control strategies e.g., nonlinear model predictive control (NMPC) or reinforcement learning (RL). This paper explores the question of what the advantages and disadvantages of the different presented prediction approaches are and how they can be used to improve the docking behavior of a vessel.

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Metadaten
Author:Hannes HomburgerORCiD, Stefan WirtensohnORCiD, Moritz DiehlORCiD, Johannes ReuterORCiD
Parent Title (English):14th IFAC Conference on Control Applications in Marine Systems, Robotics and Vehicles (CAMS 2022), September 14-16 2022, DTU Kongens Lyngby, Denmark
Document Type:Conference Proceeding
Language:English
Year of Publication:2022
Release Date:2022/11/10
Tag:Nonlinear system identification; Statistical data analysis; Maritime systems
Page Number:6
First Page:453
Last Page:458
Institutes:Institut für Systemdynamik - ISD
Relevance:Keine peer reviewed Publikation (Wissenschaftlicher Artikel und Aufsatz, Proceeding, Artikel in Tagungsband)
Open Access?:Nein
Licence (English):License LogoLizenzbedingungen Elsevier